Method for generating neural network model and control device using neural network model

ABSTRACT

A method for generating a neural network model, the method includes acquiring first time series data having a first period that is shorter than an operation period of the neural network model; extracting, from the first time series data, a plurality of sets of second time series data having a second period that is longer than the first period; and executing training on the neural network model using training data that include the plurality of sets of second time series data.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of InternationalApplication Number PCT/JP2019/008339 filed on Mar. 4, 2019 anddesignated the U.S., the entire contents of which are incorporatedherein by reference.

FIELD

The present invention relates to a method for generating a neuralnetwork model and a control device that uses a neural network model.

BACKGROUND

For instance, an engine control device or the like is a control devicethat uses a neural network model. A plant model and soft sensors, e.g.,a pressure gauge and a thermometer, are provided in the control devicein order to estimate transient operating conditions of the engine, andthe plant model and soft sensors are constituted by the neural networkmodel.

For instance, a neural network model for estimating transient operatingstates of an engine uses manipulating variables of various actuators,e.g., an engine rotation speed and a fuel injection amount, as input,and outputs a torque as a controlled variable. The neural network modelis then trained using training data including the aforementionedmanipulating variables and the torque, for instance, whereuponparameters, e.g., the weights and biases of the neural network, areadjusted. This training process needs a great amount of training data.

Generally, in a neural network that performs image classification or thelike, when gathering the training data needed for training, the amountof data is expanded by several multiples or several tens of multiples byperforming image processing, e.g., rotating or reversing acquiredimages.

CITATION LIST Patent Literature

Patent Literature 1: Japanese Patent Application Publication No.H6-274207

Patent Literature 2: Japanese Patent Application Publication No.2018-142160

However, in the case of a neural network model for estimating transientoperating states of an engine, as described above, manipulatingvariables of actuators are used as input, and a torque that is acontrolled variable is output. The manipulating variables and the torqueneeded as the training data are time series data that vary over time.Therefore, a data expansion method involving rotating and reversingimages, e.g., a method used during image classification, is not able tobe employed.

SUMMARY

An embodiment of the present invention is a method for generating aneural network model, the method comprising: acquiring first time seriesdata having a first period that is shorter than an operation period ofthe neural network model; extracting, from the first time series data, aplurality of sets of second time series data having a second period thatis longer than the first period; and executing training on the neuralnetwork model using training data that include the plurality of sets ofsecond time series data.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a view illustrating an example of time series data.

FIG. 2 is a view illustrating an example of expanded time series dataacquired by adding white noise to time series data.

FIG. 3 is a view illustrating a configuration of an NN model generationdevice for generating an NN model according to this embodiment.

FIG. 4 is a view illustrating a flowchart of an NN model training methodaccording to this embodiment.

FIG. 5 is a view illustrating an example of a virtual configuration ofan NN model.

FIG. 6 is a view illustrating a configuration of a trained NN model.

FIG. 7 is a view illustrating a configuration of an engine controldevice that uses a generated NN model.

FIG. 8 is a view illustrating examples of the time series data ofmanipulating variables.

FIG. 9 is a view illustrating a flowchart of the data expansion S2depicted on the training flowchart of FIG. 4.

FIG. 10 is a view illustrating time series data of a shorter period thanthe operation period of the NN model and short-period time series datagenerated by interpolation processing.

FIG. 11 is a view illustrating expanded time series data extracted fromthe short-period time series data at different phases.

FIG. 12 is a view illustrating specific examples of expanded time seriesdata extracted at different phases.

FIG. 13 is a view illustrating an example in which noise is generated intime series data acquired from a real engine or the like.

FIG. 14 is a view illustrating the extraction of time series dataacquired by randomly shifting the timings of the measurement points fromthe time series data.

FIG. 15 is a view illustrating an example of time series data acquiredin a real environment and expanded time series data extracted therefrom.

FIG. 16 is a view illustrating a configuration of the NN model used inthe example and the comparative example.

FIG. 17 is a view illustrating an example of cross-validation of theexample and the comparative example.

DESCRIPTION OF EMBODIMENTS

[Example of Time Series Data]

FIG. 1 is a view illustrating an example of time series data. Thisexample illustrates, for instance, the time series data of amanipulating variable that is the input, or a controlled variable thatis the output of a neural network model (referred to as an NN modelhereafter (NN being an abbreviation for Neural Network)) used toestimate transient operating states of an engine. FIG. 1 depicts threesets of time series data data01 to data03.

The time series data data01 to data03 include manipulating variables andcontrolled variables acquired at each data acquisition timing on a timeaxis. The data acquisition timings are timings at which an operationperiod, or more specifically a calculation period, of the NN model isrepeated, and are represented by triangles in FIG. 1. The axis ofordinate indicates amount of manipulating variables or controlledvariables.

The time series data are acquired from an actual engine by operating theengine on an engine bench, for instance. In other words, the time seriesdata are acquired from the engine by operating the engine in anoperation mode of a test model of the Ministry of Transport or aninternational test model. Further, the internal condition of the engineand the environmental conditions (outside air temperature, outside airpressure, and so on) may be acquired as time series data simultaneously.

A great amount of training data is needed to generate a neural networkmodel by training. As noted above, however, acquiring time series data,e.g., a manipulating variable and a controlled variable, by operating anactual engine involves a great number of processes, meaning that thereis a fixed limit on the amount of training data that are able to beacquired.

In an NN model that performs image classification, images to be used astraining data are able to be easily subjected to data expansion byrotating and reversing acquired images. In contrast to images, however,it is not possible to rotate or reverse time series data.

FIG. 2 is a view illustrating an example of expanded time series dataacquired by adding white noise to time series data. The white noise isadded to measurement values measured at respective timings of the timeseries data data01 of FIG. 1, which were acquired from a real engine, togenerate new time series data data01′ and data01″. In this case, dataexpansion is able to be performed comparatively easily.

However, the expanded time series data data01′ and data01″ are generatedby simply adding a very low amount of noise to the measurement values ofthe acquired time series data data01, and therefore the expanded timeseries data data01′ and data01″ exhibit an identical tendency to theacquired time series data data01. Hence, when the NN model is trainedusing the expanded time series data data01′ and data01″ as trainingdata, a bias may occur in the NN model based on the identical tendencyexhibited by the plurality of sets of training data. Moreover, when themagnitude of the noise added to the measurement values of the acquiredtime series data is great, the precision of the trained NN model maydecrease, and therefore setting the magnitude of the noise is not easy.

Embodiment

FIG. 3 is a view illustrating a configuration of an NN model generationdevice for generating an NN model according to this embodiment. An NNmodel generation device 1 is an information processing device, e.g., ahigh-performance computer, a server, or a personal computer. The NNmodel generation device 1 includes a CPU 10 that is a processor, a mainmemory 12 accessed by the processor, and a large capacity auxiliarystorage device 20. The auxiliary storage device 20 is a hard disk driveor a solid-state drive, for instance.

The auxiliary storage device 20 stores an NN training program 22 forcausing the processor to execute training processing on the NN model, anNN program 24 for causing the processor 10 to execute arithmeticoperations or calculations in the NN model, training data 26 used totrain the NN model, acquired time series data 28 acquired from an actualengine or the like, and so on.

The processor 10 executes NN model training by executing the NN trainingprogram 22 and the NN program 24 using the training data 26, to adjustNN model parameters, e.g., weights and biases, and thereby generates anNN model. Further, the processor 10 performs data expansion on theacquired time series data 28 by executing the NN training program 22,thereby increasing the amount of time series data included in thetraining data 26 used to train the NN model.

FIG. 4 is a view illustrating a flowchart of an NN model training methodaccording to this embodiment. The processor 10 executes the NN trainingprogram 22 in order to acquire time series data from the acquired timeseries data 28 stored in the auxiliary storage device 20 (S1). Theprocessor 10 then generates a plurality of time series data from theacquired time series data by performing data expansion, to be describedbelow (S2). The processor stores training data including the time seriesdata generated by data expansion in the auxiliary storage device 20,thereby increasing the amount of training data. At this point,preparation of the training data to be used subsequently to train the NNmodel is complete.

Next, the processor 10 executes initial adjustment of the NN model (S3).In this initial adjustment, for instance, the processor performs initialadjustments of hyperparameters, e.g., the number of neurons on eachlayer and training functions of the NN model.

The processor then executes training of the NN model by executing the NNtraining program 22 and the NN program 24 using a predetermined amountof training data (S4).

FIG. 5 is a view illustrating an example of a virtual configuration ofan NN model. In this NN model, one or a plurality of hidden layers 34are provided between an input layer 30 and an output layer 32. The NNmodel also includes a past hidden layer 36 having a type of memoryfunction for temporarily storing the values in the hidden layers 34. AnNN model that input time series data, e.g., an NN model of an engine, ispreferably a recurrent neural network that includes this type of pasthidden layer 36 having a memory function.

Taking an NN model of an engine as an example, the NN model trainingprocessing is as follows. First, manipulating variables (for instance,the engine rotation speed, the fuel injection amount, the EGR opening(the opening of an EGR valve (EGR: exhaust gas recirculation)), and theturbine opening) are input into the input layer 30 together with dataindicating the internal state of the engine, environment data, and soon. The manipulating variables are time series data in each operationperiod (arithmetic operation period or calculation period) of the NNmodel.

The processor executes arithmetic operations of each layer of the NNmodel on the input data input into the input layer 30 in a forwarddirection, and outputs a controlled variable, for instance a torque, intime series to the output layer 32 as output data. Next, the processorcalculates an error based on a difference between the controlledvariable output to the output layer 32 and a controlled variablecorresponding to the manipulating variables input of the training data.Further, the processor calculates the error on each layer byback-propagating the calculated error in the reverse direction of the NN(toward the input layer). The processor then adjusts parameters, e.g.,the weight and bias of each layer, so as to reduce the error calculatedon each layer.

The processor executes the forward-direction arithmetic operation, thereverse-direction arithmetic operation, and the parameter adjustmentdescribed above on all of the training data. At this point, theprocessor completes minibatch training. The processor typically repeatsthe minibatch training a plurality of times.

Returning to FIG. 4, after executing the minibatch training a pluralityof times (S4), for instance, the processor executes an evaluation on theNN model (S5). During NN model evaluation, the processor evaluates thetrained NN model using, for instance, evaluation data, which areseparate from the training data but include input data and output data,similarly to the training data. More specifically, the processor inputsthe input data of the evaluation data into the NN model, compares outputdata calculated therefrom with correct answer data (the output data) ofthe evaluation data, and determines whether or not the errortherebetween is less than a predetermined reference value.

When the result of the NN model evaluation is unfavorable (NO in S5),the processor returns to initial adjustment S3 of the NN. Alternatively,in a case where data expansion is to be performed (YES in S6), theprocessor returns to data expansion S2. The processor then executestraining S4 and evaluation S5 of the NN model again. When the result ofthe NN model evaluation is favorable (YES in S5), the processorterminates training of the NN model.

FIG. 6 is a view illustrating a configuration of a trained NN model. Thetrained NN model is a model generated by the NN model training describedabove. As described above, when the NN model is trained using thetraining data, adjusted internal parameters, e.g., weights and biases,are generated. The adjusted internal parameters are included in the NNprogram 24 for executing arithmetic operations on the NN model.

An NN model 40 generated by training includes a processor 42, a memory44 accessed by the processor, an input/output unit 46 which, at the timeof inference (i.e., during an actual operation), receives input data asinput and outputs output data of the NN model, a bus 48, and anauxiliary storage device for storing the NN program 24. When theprocessor 42 executes the NN program 24, the NN model 40 is realized topredict an output.

FIG. 7 is a view illustrating a configuration of an engine controldevice that uses a generated NN model. An engine control device 50includes an engine inference unit 51 constituted by an NN model, and atarget controlled variable setting unit 54 for setting a torque S54 thatis a target controlled variable based on manipulations of an acceleratorpedal or the like by a driver. The engine control device 50 alsoincludes a control unit 52 for calculating a difference S52 between apredicted torque S51 that is a predicted controlled variable output bythe engine inference unit 51 and a target torque S54 that is a targetcontrolled variable, and an actuator manipulating variable generationunit 53 for determining or generating a manipulating variable of anactuator provided in the engine based on the torque difference S52. Theactuator operation amount generation unit 53 calculates how to varywhich manipulating variable in order to eliminate the torque differenceS52, and outputs a manipulation signal S55 corresponding to the variedmanipulating variable S55 to an actual engine system 55. In responsethereto, the actual engine system 55 outputs a torque that is thecontrolled variable.

The engine inference unit 51 is the trained NN model 40. Time seriesdata of the manipulating variable output S55 to the actual engine system55 are input into the NN model 40, and data indicating the internalstate of the actual engine and environment data (temperature, pressure,and so on) may also be input into the NN model 40.

Torque, which is the controlled variable generated by the actual enginesystem 55, is difficult to detect using a sensor. In the engine controldevice, therefore, the NN model 40 is used as the engine inference unit51, and the controlled variable (the torque) calculated by the NN model40, which has been supplied with a manipulating variable S55 in anidentical manner to the actual engine system 55, is predicted as thecontrolled variable (the torque) to be output by the actual enginesystem 55.

[Data Expansion Method]

FIG. 8 is a view illustrating examples of the time series data ofmanipulating variables. The manipulating variables, for instance theengine rotation speed, the fuel injection amount, the EGR opening, andthe injection timing, are constituted by chirp signals having afrequency component that varies over time, as illustrated in FIG. 8. Therotation speed, for instance, increases and decreases in the form of asine wave, and the frequency of the sine wave varies over time. Theother manipulating variables are identical.

FIG. 9 is a view illustrating a flowchart of the data expansion S2depicted on the training flowchart of FIG. 4. Data expansion includes aplurality of data expansion processes, for instance (1) processing S11and S12 for acquiring time series data having a period that is shorterthan the operation period of the NN model by performing interpolationprocessing on time series data acquired from a real engine or the like,(2) processing S13 and S14 for increasing the amount of time series databy extracting a plurality of sets of time series data having apredetermined period from the short-period time series data at differentphases, (3) processing S15 and S16 for increasing the amount of timeseries data by extracting time series data acquired by shifting themeasurement points of the time series data randomly along the time axis,(4) processing S17 and S18 for generating separate time series data byadding white noise to the values of the time series data, and so on.

In the processing S11 and S12 of section (1) above, when the period ofthe time series data acquired from the actual engine or the like isequal to the operation period of the NN model (S11), data acquired byinterpolation are added thereto so as to acquire time series data havinga period that is shorter than the operation period of the NN model(S12).

FIG. 10 is a view illustrating time series data having a shorter periodthan the operation period of the NN model and short-period time seriesdata generated by interpolation processing. In FIG. 10, the timings ofthe time series data are indicated by triangles, while the values of thetime series data are not depicted.

Acquired time series data data01_a are time series data having a shorterperiod than an operation period T, and include data of the operationperiod T of the NN model and data having a shorter period than theoperation period T. When it is possible to acquire the time series datadata01_a from the actual engine or the like, the interpolationprocessing does not have to be performed (NO in S11).

Acquired time series data data00, meanwhile, are data having theoperation period T of the NN model. In this case, the interpolationprocessing is to be performed (YES in S11), and therefore the processorgenerates interpolated time series data data01_b by generatinginterpolated data (data acquired at timings indicated by white trianglesin the figure) in the time axis direction between the data (dataacquired at timings indicated by black triangles in the figure) of theperiod T, which constitute the acquired time series data data00, andadding the interpolated data thereto. The interpolated time series datadata01_b include data having a period that is shorter than the operationperiod T of the NN model.

FIG. 11 is a view illustrating expanded time series data extracted fromthe short-period time series data at different phases. Next, when dataexpansion is to be performed by extracting data at different phases (YESin S13), the processor generates a plurality of sets of expanded timeseries data by extracting a plurality of sets of time series datadata01_1 to data01_5 having a predetermined period at different phasesfrom time series data data01 having a shorter period than the operationperiod T of the NN model (S14).

The short-period time series data data01 are identical to the timeseries data data01_a and data01_b of FIG. 10 and include data (the blacktriangles) acquired at timings corresponding to the operation period Tof the NN model and short-period data (the white triangles) acquiredbetween the data having the operation period T of the NN model. On theother hand, the expanded time series data data01_1 to data01_5 are timeseries data acquired by extracting data having a predetermined periodfrom the short-period time series data data01 at shifted phases. In theexample of FIG. 11, the expanded time series data are data of the sameperiod as the operation period T of the NN model. In this embodiment,although the predetermined period of the expanded time series data doesnot necessarily have to be the same as the operation period T, thepredetermined period is at least longer than the period of theshort-period time series data data01.

FIG. 12 is a view illustrating specific examples of expanded time seriesdata extracted at different phases. FIG. 12 depicts expanded time seriesdata data01_1, data01_2, and data01_3 having the same period as theoperation period T of the NN model, which have been extracted atrespectively shifted phases from the data at the sample timings of theshort-period time series data data01. The short-period time series datadata01 are sine-wave time series data.

The expanded time series data data01_1 are generated by extracting dataat sample timings indicated by squares, the expanded time series datadata01_2 are generated by extracting data at sample timings indicated bytriangles, and the expanded time series data data01_3 are generated byextracting data at sample timings indicated by circles.

The time series data data01 are sine wave data, and therefore theexpanded time series data data01_1 to data01_3 are time series data ofthe same period as the operation period T of the NN model but withrespectively different data values. Hence, the expanded time series dataexhibit a plurality of mutually different tendencies, and therefore atrained NN model that uses these expanded time series data as trainingdata is a high generalization ability model in which bias is suppressed.

FIG. 13 is a view illustrating an example in which noise is generated intime series data acquired from a real engine or the like. In the figure,time series data data01_c represent an example in which noise indicatedby rhomboid is generated at timings indicated by squares. This noise issudden or periodic noise originating from the measurement environmentwhen time series data are acquired from an actual engine or the like inan actual environment, for instance. Even when sudden or periodic noiseis included, as in the acquired time series data data01_c, by extractingdata of a predetermined period at different phases from the time seriesdata data01_c so as to generate expanded time series data, it ispossible to acquire expanded time series data (data01_2 and data01_3 inFIG. 12) not including the noise.

Hence, although data caused by noise are included in time series datadata01_1 n depicted in FIG. 13, among the expanded time series data,data caused by noise are not included in the time series data (data01_2and data01_3 in FIG. 12) acquired at timings deviating from the timingof the noise. Therefore, when time series data acquired in a realenvironment include data caused by sudden or periodic noise, it can besuppressed that a situation in which the NN model is overtrained bybeing trained using the time series data data01_1 n that includes thenoise data.

FIG. 14 is a view illustrating the extraction of time series dataacquired by randomly shifting the timings of the measurement points fromtime series data. When time series data acquired by shifting the timingsof the measurement points are to be generated (S15), the processorextracts, from the short-period time series data data01, data data_sftat timings randomly shifted earlier or later than the timings (the blacktriangles in the figure) of the predetermined period T along the timeaxis, and thereby acquires expanded time series data data01_1 s (S16).

When training is performed using the time series data data01_1 s, whichinclude the data data_sft having the shifted extraction timings, as thetraining data, the trained NN model is a high generalization abilitymodel that allows time series data having a shifted measurement timing,which may be generated in an actual environment. Hence, in addition todata expansion, it is possible to generate training data with which thegeneralization ability is increased.

Returning to FIG. 9, when time series data with added white noise are tobe generated by data expansion (YES in S17), the processor generatesseparate time series data by adding white noise to the values of thetime series data (S18). Finally, the processor stores the plurality ofextracted time series data in the memory (S19).

Examples of the Embodiment

In a test conducted by the present inventors, a normal operating patternof the Ministry of Transport and transient operation data were used asthe training data of the NN model. The transient operation data wereacquired by continuously varying operating conditions based on chirpsignals being set upper and lower limits for each manipulating variable.Examples of the chirp signals are depicted in FIG. 8.

FIG. 15 is a view illustrating an example of time series data acquiredin a real environment and expanded time series data extracted therefrom.In this example, the operation period of the NN model was 0.8 seconds,and the time series data data01 of the manipulating variables and thecontrolled variable were acquired at intervals of 0.08 seconds, i.e., ashorter period than the operation period of the NN model. Three sets oftime series data data01_1 to data01_3 having the same period as theoperation period of the NN model, i.e., 0.8 seconds, were extracted atphases acquired by shifting the extraction start time by 0.08 seconds,0.32 seconds, and 0.64 seconds, respectively, from the acquired timeseries data data01 and these data were included in the training data.

In a comparative example, meanwhile, time series data extracted in aperiod of 0.8 seconds from an extraction start time of 0.08 seconds wereused as the training data. Further, the time series data constitutingthe training data were acquired by operating a 3L series four-cylinderdiesel engine on an engine bench.

FIG. 16 is a view illustrating a configuration of the NN model used inthe example of the embodiment and the comparative example. The NN modelincludes an input layer 30, hidden layers 34_a and 34_b, and an outputlayer 32. Four controlled variables, namely the rotation speed, the fuelinjection amount, the EGR opening, and the turbine opening, are inputinto the input layer, and a controlled variable, for instance an intakemanifold pressure, is output from the output layer. The number ofneurons in each of the hidden layers is 10, and an activation functionand a linear function of the hidden layers are as illustrated.

FIG. 17 is a view illustrating an example of cross-validation of theexample of the embodiment and the comparative example. Cross-validationwas performed on the trained NN models of the example of the embodimentand the comparative example using data acquired by operating an enginein an operation mode of the WHTC (World Harmonized Vehicle Cycle),whereupon the fitness, the RMSE (Root Mea Square Error), and R² weredetermined. It is able to be ascertained from the respective evaluationvalues illustrated in FIG. 17 that the fitness value, the RMSE, and R²of the example of the embodiment are all superior to those of thecomparative example.

Outputs (predicted values) calculated by inputting into the NN modelinput values of data acquired from an engine operated in the operationmode of the WHTC and outputs (correct answer values) of the acquireddata are plotted on the figures illustrating correct answer values andprediction values in FIG. 17. When the horizontal axis is set as x andthe vertical axis is set as y, more data are plotted on the y=x axis inthe example of the embodiment than in the comparative example, and thisproves the superiority of the example.

According to this embodiment, as described above, short-period timeseries data having a period that is shorter than the operation period ofthe NN model are acquired. Then, the training data are expanded byadding a plurality of sets of time series data acquired by extractingdata having a predetermined period from the short-period time seriesdata at different phases, and time series data acquired by extracting,from the short-period time series data, data acquired at timings shiftedrandomly forward or backward along the time axis. Hence, time seriesdata exhibiting various tendencies are able to be easily generated fromthe acquired time series data, and as a result, a great amount oftraining data is able to be generated. By training the NN model usingthese training data, it is possible to generate a high generalizationability NN model that is not overtrained.

According to the first aspect, the time series data included in thetraining data are able to be expanded easily.

All examples and conditional language provided herein are intended forthe pedagogical purposes of aiding the reader in understanding theinvention and the concepts contributed by the inventor to further theart, and are not to be construed as limitations to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although one or more embodiments of thepresent invention have been described in detail, it should be understoodthat the various changes, substitutions, and alterations could be madehereto without departing from the spirit and scope of the invention.

REFERENCE SIGNS LIST

1: Neural network model generation device

22: NN learning program

24: NN program

26: Training data

28: Time series data

data01: Short-period time series data

data01_1 to data01_3: Expanded time series data

1. A method for generating a neural network model, the methodcomprising: acquiring first time series data having a first period thatis shorter than an operation period of the neural network model;extracting, from the first time series data, a plurality of sets ofsecond time series data having a second period that is longer than thefirst period; and executing training on the neural network model usingtraining data that include the plurality of sets of second time seriesdata.
 2. The method for generating a neural network model according toclaim 1, wherein, during the extraction process, a plurality of sets ofsecond time series data having different phases are extracted from thefirst time series data.
 3. The method for generating a neural networkmodel according to claim 1, wherein, during the extraction process, dataacquired at time points shifted forward or backward from a time pointcorresponding to the second period are extracted from the first timeseries data, such that the second time series data in which the secondperiod is partially shorter or longer are extracted.
 4. The method forgenerating a neural network model according to claim 1, wherein theacquisition process includes: acquiring third time series data havingthe operation period of the neural network model; and generating thefirst time series data by performing interpolation between data that areadjacent on a time axis of the third time series data so as to add aplurality of time series data between the data that are adjacent to thethird time series data.
 5. The method for generating a neural networkmodel according to claim 1, further comprising: evaluating the trainedneural network model following the training process; and increasing theamount of the second time series data by executing the extractionprocess when an evaluation acquired during the evaluation process doesnot reach a reference level, wherein, during the training process,training of the neural network model is executed using training dataincluding the increased second time series data.
 6. The method forgenerating a neural network model according to claim 1, wherein thefirst time series data are time series data of an input or an output ofthe neural network model.
 7. The method for generating a neural networkmodel according to claim 6, wherein the time series data of the inputare constituted by a chirp signal having a varying frequency.
 8. Themethod for generating a neural network model according to claim 1,wherein the neural network model is a recurrent neural network.
 9. Acontrol device using a neural network model, the control devicecomprising a neural network model generated by the method for generatinga neural network model according to claim
 1. 10. The control deviceusing a neural network model according to claim 9, wherein the neuralnetwork model is a model for predicting a state of an engine, thecontrol device further comprising a manipulating variable calculationgeneration unit for calculating a manipulating variable to be input intothe engine based on an output of the neural network model.